{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import util\n",
    "from torchvision.datasets import DatasetFolder as DatasetFolder\n",
    "from tqdm.notebook import tqdm\n",
    "from multiprocessing import Pool\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import os\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_PATH = 'D:\\\\forecasting\\\\train-002'\n",
    "# DATA_PATH = 'E:\\\\data\\\\rainfall'\n",
    "\n",
    "p_num = 4\n",
    "p = Pool(p_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = util.get_file_names_in_folder(DATA_PATH, \"npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = np.linspace(0, len(l)//20, p_num+1).astype(int)\n",
    "\n",
    "part_of_files = [l[idx[i]:idx[i+1]] for i in range(p_num)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = p.map(util.load_npy_files, part_of_files)\n",
    "\n",
    "npy_arr = np.concatenate(result, axis=0).reshape(-1,1600, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# type :앞자리 0: Ocean, 앞자리 1: Land, 앞자리 2: Coastal, 앞자리 3: Inland Water\n",
    "columns = ['CH1', 'CH2', 'CH3', 'CH4', 'CH5', 'CH6', 'CH7', 'CH8', 'CH9', 'types', \n",
    "          'GMI Longitude', 'GMI Latitude', 'DPR Longitude', 'DPR Latitude', 'Precipitation']\n",
    "a = pd.DataFrame(npy_arr.reshape(-1, 15), columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CH1</th>\n",
       "      <th>CH2</th>\n",
       "      <th>CH3</th>\n",
       "      <th>CH4</th>\n",
       "      <th>CH5</th>\n",
       "      <th>CH6</th>\n",
       "      <th>CH7</th>\n",
       "      <th>CH8</th>\n",
       "      <th>CH9</th>\n",
       "      <th>types</th>\n",
       "      <th>GMI Longitude</th>\n",
       "      <th>GMI Latitude</th>\n",
       "      <th>DPR Longitude</th>\n",
       "      <th>DPR Latitude</th>\n",
       "      <th>Precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.107200e+06</td>\n",
       "      <td>6.107200e+06</td>\n",
       "      <td>6.107200e+06</td>\n",
       "      <td>6.107200e+06</td>\n",
       "      <td>6.107200e+06</td>\n",
       "      <td>6.107200e+06</td>\n",
       "      <td>6.107200e+06</td>\n",
       "      <td>6.107200e+06</td>\n",
       "      <td>6.107200e+06</td>\n",
       "      <td>6.107200e+06</td>\n",
       "      <td>6.107200e+06</td>\n",
       "      <td>6.107200e+06</td>\n",
       "      <td>6.107200e+06</td>\n",
       "      <td>6.107200e+06</td>\n",
       "      <td>6.107200e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.933552e+02</td>\n",
       "      <td>1.366959e+02</td>\n",
       "      <td>2.089779e+02</td>\n",
       "      <td>1.591119e+02</td>\n",
       "      <td>2.266358e+02</td>\n",
       "      <td>2.240897e+02</td>\n",
       "      <td>1.807998e+02</td>\n",
       "      <td>2.511872e+02</td>\n",
       "      <td>2.276419e+02</td>\n",
       "      <td>3.193244e+01</td>\n",
       "      <td>1.395827e+02</td>\n",
       "      <td>3.087936e+01</td>\n",
       "      <td>1.395827e+02</td>\n",
       "      <td>3.087973e+01</td>\n",
       "      <td>-1.763978e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.886547e+01</td>\n",
       "      <td>6.737188e+01</td>\n",
       "      <td>3.011623e+01</td>\n",
       "      <td>5.487033e+01</td>\n",
       "      <td>2.448280e+01</td>\n",
       "      <td>1.941577e+01</td>\n",
       "      <td>4.057915e+01</td>\n",
       "      <td>2.571985e+01</td>\n",
       "      <td>3.211722e+01</td>\n",
       "      <td>5.411251e+01</td>\n",
       "      <td>1.980154e+01</td>\n",
       "      <td>1.431385e+01</td>\n",
       "      <td>1.980137e+01</td>\n",
       "      <td>1.431287e+01</td>\n",
       "      <td>4.205014e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.551603e+02</td>\n",
       "      <td>7.910158e+01</td>\n",
       "      <td>1.699718e+02</td>\n",
       "      <td>9.097734e+01</td>\n",
       "      <td>1.795542e+02</td>\n",
       "      <td>1.717506e+02</td>\n",
       "      <td>1.225597e+02</td>\n",
       "      <td>1.245568e+02</td>\n",
       "      <td>1.218110e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.037079e+02</td>\n",
       "      <td>4.322756e+00</td>\n",
       "      <td>1.037087e+02</td>\n",
       "      <td>4.500694e+00</td>\n",
       "      <td>-9.999900e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.681031e+02</td>\n",
       "      <td>9.226089e+01</td>\n",
       "      <td>1.863736e+02</td>\n",
       "      <td>1.186110e+02</td>\n",
       "      <td>2.067405e+02</td>\n",
       "      <td>2.100692e+02</td>\n",
       "      <td>1.498544e+02</td>\n",
       "      <td>2.373014e+02</td>\n",
       "      <td>2.006184e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.224125e+02</td>\n",
       "      <td>1.851429e+01</td>\n",
       "      <td>1.224142e+02</td>\n",
       "      <td>1.851558e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.722749e+02</td>\n",
       "      <td>9.678005e+01</td>\n",
       "      <td>1.976235e+02</td>\n",
       "      <td>1.349000e+02</td>\n",
       "      <td>2.289588e+02</td>\n",
       "      <td>2.187383e+02</td>\n",
       "      <td>1.652203e+02</td>\n",
       "      <td>2.582140e+02</td>\n",
       "      <td>2.334053e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.398260e+02</td>\n",
       "      <td>3.122711e+01</td>\n",
       "      <td>1.398065e+02</td>\n",
       "      <td>3.122579e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.434707e+02</td>\n",
       "      <td>2.221768e+02</td>\n",
       "      <td>2.406149e+02</td>\n",
       "      <td>2.227629e+02</td>\n",
       "      <td>2.436530e+02</td>\n",
       "      <td>2.319810e+02</td>\n",
       "      <td>2.129718e+02</td>\n",
       "      <td>2.703181e+02</td>\n",
       "      <td>2.538294e+02</td>\n",
       "      <td>1.020000e+02</td>\n",
       "      <td>1.566874e+02</td>\n",
       "      <td>4.348771e+01</td>\n",
       "      <td>1.566896e+02</td>\n",
       "      <td>4.348815e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.627186e+02</td>\n",
       "      <td>1.246589e+03</td>\n",
       "      <td>3.631808e+02</td>\n",
       "      <td>3.949387e+02</td>\n",
       "      <td>3.047290e+02</td>\n",
       "      <td>3.039225e+02</td>\n",
       "      <td>3.252310e+02</td>\n",
       "      <td>3.027523e+02</td>\n",
       "      <td>3.548882e+02</td>\n",
       "      <td>3.220000e+02</td>\n",
       "      <td>1.761738e+02</td>\n",
       "      <td>5.579985e+01</td>\n",
       "      <td>1.761748e+02</td>\n",
       "      <td>5.579247e+01</td>\n",
       "      <td>2.997408e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                CH1           CH2           CH3           CH4           CH5  \\\n",
       "count  6.107200e+06  6.107200e+06  6.107200e+06  6.107200e+06  6.107200e+06   \n",
       "mean   1.933552e+02  1.366959e+02  2.089779e+02  1.591119e+02  2.266358e+02   \n",
       "std    3.886547e+01  6.737188e+01  3.011623e+01  5.487033e+01  2.448280e+01   \n",
       "min    1.551603e+02  7.910158e+01  1.699718e+02  9.097734e+01  1.795542e+02   \n",
       "25%    1.681031e+02  9.226089e+01  1.863736e+02  1.186110e+02  2.067405e+02   \n",
       "50%    1.722749e+02  9.678005e+01  1.976235e+02  1.349000e+02  2.289588e+02   \n",
       "75%    2.434707e+02  2.221768e+02  2.406149e+02  2.227629e+02  2.436530e+02   \n",
       "max    6.627186e+02  1.246589e+03  3.631808e+02  3.949387e+02  3.047290e+02   \n",
       "\n",
       "                CH6           CH7           CH8           CH9         types  \\\n",
       "count  6.107200e+06  6.107200e+06  6.107200e+06  6.107200e+06  6.107200e+06   \n",
       "mean   2.240897e+02  1.807998e+02  2.511872e+02  2.276419e+02  3.193244e+01   \n",
       "std    1.941577e+01  4.057915e+01  2.571985e+01  3.211722e+01  5.411251e+01   \n",
       "min    1.717506e+02  1.225597e+02  1.245568e+02  1.218110e+02  0.000000e+00   \n",
       "25%    2.100692e+02  1.498544e+02  2.373014e+02  2.006184e+02  0.000000e+00   \n",
       "50%    2.187383e+02  1.652203e+02  2.582140e+02  2.334053e+02  0.000000e+00   \n",
       "75%    2.319810e+02  2.129718e+02  2.703181e+02  2.538294e+02  1.020000e+02   \n",
       "max    3.039225e+02  3.252310e+02  3.027523e+02  3.548882e+02  3.220000e+02   \n",
       "\n",
       "       GMI Longitude  GMI Latitude  DPR Longitude  DPR Latitude  Precipitation  \n",
       "count   6.107200e+06  6.107200e+06   6.107200e+06  6.107200e+06   6.107200e+06  \n",
       "mean    1.395827e+02  3.087936e+01   1.395827e+02  3.087973e+01  -1.763978e+01  \n",
       "std     1.980154e+01  1.431385e+01   1.980137e+01  1.431287e+01   4.205014e+02  \n",
       "min     1.037079e+02  4.322756e+00   1.037087e+02  4.500694e+00  -9.999900e+03  \n",
       "25%     1.224125e+02  1.851429e+01   1.224142e+02  1.851558e+01   0.000000e+00  \n",
       "50%     1.398260e+02  3.122711e+01   1.398065e+02  3.122579e+01   0.000000e+00  \n",
       "75%     1.566874e+02  4.348771e+01   1.566896e+02  4.348815e+01   0.000000e+00  \n",
       "max     1.761738e+02  5.579985e+01   1.761748e+02  5.579247e+01   2.997408e+02  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0.000000       5807708\n",
       "-9999.900391      10818\n",
       " 0.306519            67\n",
       " 0.507415            63\n",
       " 0.276415            63\n",
       " 0.222078            63\n",
       " 0.320526            63\n",
       " 0.293028            62\n",
       " 0.350151            62\n",
       " 0.341462            62\n",
       " 0.501456            62\n",
       " 0.504428            61\n",
       " 0.370380            61\n",
       " 0.280037            60\n",
       " 0.289266            60\n",
       " 0.219096            59\n",
       " 0.199144            59\n",
       " 0.322570            59\n",
       " 0.416312            57\n",
       " 0.220583            57\n",
       " 0.335064            56\n",
       " 0.257189            55\n",
       " 0.226615            55\n",
       " 0.214685            55\n",
       " 0.294924            55\n",
       " 0.314458            55\n",
       " 0.267532            55\n",
       " 0.223582            54\n",
       " 0.264046            54\n",
       " 0.287400            54\n",
       "                 ...   \n",
       " 0.898918             1\n",
       " 1.017628             1\n",
       " 3.887730             1\n",
       " 0.881255             1\n",
       " 1.017796             1\n",
       " 0.193947             1\n",
       " 1.017556             1\n",
       " 3.888308             1\n",
       " 0.898444             1\n",
       " 1.017429             1\n",
       " 1.017429             1\n",
       " 0.902142             1\n",
       " 1.017863             1\n",
       " 1.018328             1\n",
       " 1.018229             1\n",
       " 1.018320             1\n",
       " 0.901014             1\n",
       " 0.901108             1\n",
       " 3.891580             1\n",
       " 1.018320             1\n",
       " 1.018297             1\n",
       " 3.891644             1\n",
       " 3.886900             1\n",
       " 3.891796             1\n",
       " 0.901837             1\n",
       " 1.018220             1\n",
       " 1.018070             1\n",
       " 0.901394             1\n",
       " 1.017901             1\n",
       " 0.150978             1\n",
       "Name: Precipitation, Length: 117285, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.Precipitation.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x720 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "util.display_imgs(s.transpose(2,0,1)[:14,:,:], cols=5, figsize=(10, 5))\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
